Thursday, November 17th
5:45 PM Registration
6:00 PM Seminar Begins
7:30 PM Reception
Much of this talk will come from joint work I did with Jianqing Fan at Princeton and Wei Dai now at Dimensional Fund Advisors. We set out to provide a purely data-driven analysis of the volatility risk premium, using tools from high-frequency finance and Big Data analytics. We argue that the volatility risk premium, loosely defined as the difference between realized and implied volatility, can best be understood when viewed as a systematically priced bias. We first use ultra-high-frequency transaction data on SPDRs and a novel approach for estimating integrated volatility on the frequency domain to compute realized volatility. From that we subtract the daily VIX, our measure of implied volatility, to construct a time series of the volatility risk premium. To identify the factors behind the volatility risk premium as a priced bias we decompose it into magnitude and direction. We find compelling evidence that the magnitude of the deviation of the realized volatility from implied volatility represents supply and demand imbalances in the market for hedging tail risk. It is difficult to conclusively accept the hypothesis that the direction or sign of the volatility risk premium reflects expectations about future levels of volatility. However, evidence supports the hypothesis that the sign of the volatility risk premium is indicative of gains or losses on a delta-hedged portfolio consistent with Bakshi and Kapadia (2003).
As someone who has come from a background in financial modeling but has developed a penchant for data science and analytics, I will spend some time at the end of my talk on my thoughts about how data science is being embraced (in some ways, and eschewed in others) by the quantitative finance community.
Michael B. Imerman is the Theodore A. Lauer Distinguished Professor of Investments and Assistant Professor in the Perella Department of Finance at Lehigh University. Dr. Imerman’s previous appointments were at Princeton in the ORFE Department and Rutgers Business School from where he received his Ph.D. Before coming to academia, Imerman worked as an analyst at Lehman Brothers supporting the high grade credit and credit derivative trading desks.
At Lehigh, Professor Imerman teaches Derivatives and Risk Management both at the undergraduate and graduate levels. His primary research area is in credit risk modeling with applications to banking, risk management, and financial regulation. Most recently he has been actively involved in integrating data science techniques into the evaluation of risk in the securitized mortgage market.
About the Series
The IAQF's Thalesians Seminar Series is a joint effort on the part of the IAQF (www.iaqf.org) and the Thalesians (www.thalesians.com). The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion.